Remote AI Research Engineer — LLM & Multimodal

Tether; United Kingdom; Remote; GBP 111,000 - 141,000; Full time

Job summary

Tether's AI model team is seeking a seasoned researcher to drive architecture development for large-scale LLM and multi-modal systems. You will push state-of-the-art pre-training and alignment while scaling training across large GPU clusters.

You will apply your deep knowledge of transformers, distributed frameworks, and PyTorch/Hugging Face to deliver robust, efficient models. This remote, international role invites collaboration with researchers worldwide on cutting-edge AI R&D.

Qualifications

• PhD in NLP/ML preferred with publications in top AI conferences.
• Experience with large-scale LLM/Multi-Modal pre-training on thousands of GPUs.
• Experience with distributed training frameworks and tools.
• Expertise in transformers and related model improvements.
• Proficiency with PyTorch and Hugging Face for development and deployment.

Responsibilities

• Large-Scale Pre-Training: Conduct foundational pre-training for LLMs and Multi-Modal models on large, distributed servers with thousands of GPUs.
• Architecture & Alignment Innovation: Design, prototype, and scale innovative architectures and cross-modal alignment layers.
• Data Strategy: Source, filter, and curate massive-scale textual and multi-modal datasets.
• Experimental Research: Independently and collaboratively run experiments and refine training methods.
• Optimization & Debugging: Identify bottlenecks and improve model efficiency and stability during long runs.
• System Scalability: Contribute to distributed training systems for scalable hardware utilization.

Skills

Large-scale LLM pre-training

Multi-Modal models

Distributed training

Transformer architectures

PyTorch & Hugging Face

Education

PhD in NLP/ML

Computer Science degree

Job description

Tether's AI model team is seeking a seasoned researcher to drive architecture development for large-scale LLM and multi-modal systems. You will push state-of-the-art pre-training and alignment while scaling training across large GPU clusters.

You will apply your deep knowledge of transformers, distributed frameworks, and PyTorch/Hugging Face to deliver robust, efficient models. This remote, international role invites collaboration with researchers worldwide on cutting-edge AI R&D.